When more is less: An examination of the relationship between hours in telework and role overload
Bibliographic record
Abstract
BACKGROUND: Proponents of telework arrangements assert that those who telework have more control over their work and family domains than their counterparts who are not permitted to work from home. OBJECTIVE: Using Karasek's theory we hypothesized that the relationship between demands (hours in work per week; hours in childcare per week) and strain (work role overload; family role overload) would be moderated by the number of hours the employee spent per week teleworking (control). METHODS: To determine how the number of telework hours relates to work role overload and family role overload, we follow the test for moderation and mediation using hierarchical multiple regression analysis as outlined by Frazier et al. [50] We used survey data collected from 1,806 male and female professional employees who spent at least one hour per week working from home during regular hours (i.e. teleworking). RESULTS: As hypothesized, the number of hours in telework per week negatively moderated the relation between work demands (total hours in paid employment per week) and work strain (work role overload). Contrary to our hypothesis, the number of hours in telework per week only partially mediated the relation between family demands (hours a week in childcare) and family role overload (strain). CONCLUSIONS: The findings from this study support the idea that the control offered by telework is domain specific (helps employees meet demands at work but not at home).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".